ReviewJACS Au2022
From Platform to Knowledge Graph: Evolution of Laboratory Automation.
Review in JACS Au, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed.
- High-throughput solid microsampling through stochastic robotic automation.Communications chemistry · 2026Article
- From Eyes to Cameras to Kinetics: Computer Vision for Chemical Process Monitoring.Accounts of chemical research · 2026Article
- Database utility for cyclovoltammetry knowledge (DUCK): unified platform for electrochemical data.Digital discovery · 2026Article
- The ADePT framework for assessing autonomous laboratory robotics.Communications chemistry · 2026Review
- Self-Driving Development of Perfusion Processes for Monoclonal Antibody Production.Biotechnology and bioengineering · 2026Article
- Artificial intelligence-powered microscopy: Transforming the landscape of parasitology.Journal of microscopy · 2026Review
- Artificial Intelligence Empowered New Materials: Discovery, Synthesis, Prediction to Validation.Nano-micro letters · 2026Review
- NOCTIS: open-source toolkit that turns reaction data into actionable graph networks.Journal of cheminformatics · 2025Article
- Review
- Revolutionizing Laboratory Practices: Pioneering Trends in Total Laboratory Automation.Annals of laboratory medicine · 2025Review
- Deep video anomaly detection in automated laboratory setting.Expert systems with applications · 2025Article
- Science acceleration and accessibility with self-driving labs.Nature communications · 2025Review
- Self-Driving Laboratories for Chemistry and Materials Science.Chemical reviews · 2024Review
- Digitalization concepts in academic bioprocess development.Engineering in life sciences · 2024Review
- AutonoMS: Automated Ion Mobility Metabolomic Fingerprinting.Journal of the American Society for Mass Spectrometry · 2024Article
- A dynamic knowledge graph approach to distributed self-driving laboratories.Nature communications · 2024Article
- Chemical Species Ontology for Data Integration and Knowledge Discovery.Journal of chemical information and modeling · 2023Article
- An Ecosystem for Digital Reticular Chemistry.ACS central science · 2023Article
- Knowledge Engineering in Chemistry: From Expert Systems to Agents of Creation.Accounts of chemical research · 2023Article
- OntoPESScan: An Ontology for Potential Energy Surface Scans.ACS omega · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-fidelity computer-aided experimentation is becoming more accessible with the development of computing power and artificial intelligence tools. The advancement of experimental hardware also empowers researchers to reach a level of accuracy that was not possible in the past. Marching toward the next generation of self-driving laboratories, the orchestration of both resources lies at the focal point of autonomous discovery in chemical science. To achieve such a goal, algorithmically accessible data representations and standardized communication protocols are indispensable. In this perspective, we recategorize the recently introduced approach based on Materials Acceleration Platforms into five functional components and discuss recent case studies that focus on the data representation and exchange scheme between different components. Emerging technologies for interoperable data representation and multi-agent systems are also discussed with their recent applications in chemical automation. We hypothesize that knowledge graph technology, orchestrating semantic web technologies and multi-agent systems, will be the driving force to bring data to knowledge, evolving our way of automating the laboratory.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.